



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType

class   snake_case__							(_lowerCAmelCase						):







						"""simple docstring"""




						def __init__(  self   ,  __lowercase    )   -> int:
									"""simple docstring"""




									a__  :		List[str]					=    data

						def __iter__(  self    )   -> int:
									"""simple docstring"""




									for element in self.data:
												yield element




def       lowerCAmelCase_	(    _lowercase							:  Optional[int]=True)				->			Tuple:







			"""simple docstring"""
			a__  :		Optional[int]					=    Accelerator(even_batches=lowerCAmelCase__)
			assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
			return accelerator

def       lowerCAmelCase_	(    _lowercase							:  Accelerator		,  _lowercase							:  int		,  _lowercase							:  int		,  _lowercase							:  bool = False)				->			Union[str, Any]:







			"""simple docstring"""


			if iterable:
						a__  :		Tuple					=    DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase__)))
			else:
						a__  :		Tuple					=    TensorDataset(torch.as_tensor(range(lowerCAmelCase__)))

			a__  :		str					=    DataLoader(lowerCAmelCase__		,  batch_size=lowerCAmelCase__)
			a__  :		Tuple					=    accelerator.prepare(lowerCAmelCase__)

			return dl

def       lowerCAmelCase_	(    _lowercase							:  Accelerator		,  _lowercase							:  int		,  _lowercase							:  int		,  _lowercase							:  List[int]		,  _lowercase							:  List[int]		,  )				->			Dict:







			"""simple docstring"""
			a__  :		List[str]					=    create_dataloader(accelerator=lowerCAmelCase__		,  dataset_size=lowerCAmelCase__		,  batch_size=lowerCAmelCase__)

			a__  :		Optional[Any]					=    [len(batch[0]) for batch in dl]

			if accelerator.process_index == 0:
						assert batch_sizes == process_0_expected_batch_sizes
			elif accelerator.process_index == 1:
						assert batch_sizes == process_1_expected_batch_sizes

def       lowerCAmelCase_	(    )				->			List[Any]:







			"""simple docstring"""
			a__  :		Optional[Any]					=    create_accelerator()

			# without padding, we would expect a different number of batches
			verify_dataloader_batch_sizes(
			    lowerCAmelCase__		,  dataset_size=3		,  batch_size=1		,  process_0_expected_batch_sizes=[1, 1]		,  process_1_expected_batch_sizes=[1, 1]		,  )

			# without padding, we would expect the same number of batches, but different sizes
			verify_dataloader_batch_sizes(
			    lowerCAmelCase__		,  dataset_size=7		,  batch_size=2		,  process_0_expected_batch_sizes=[2, 2]		,  process_1_expected_batch_sizes=[2, 2]		,  )

def       lowerCAmelCase_	(    )				->			List[Any]:







			"""simple docstring"""
			a__  :		str					=    create_accelerator(even_batches=lowerCAmelCase__)

			verify_dataloader_batch_sizes(
			    lowerCAmelCase__		,  dataset_size=3		,  batch_size=1		,  process_0_expected_batch_sizes=[1, 1]		,  process_1_expected_batch_sizes=[1]		,  )

			verify_dataloader_batch_sizes(
			    lowerCAmelCase__		,  dataset_size=7		,  batch_size=2		,  process_0_expected_batch_sizes=[2, 2]		,  process_1_expected_batch_sizes=[2, 1]		,  )

def       lowerCAmelCase_	(    )				->			Union[str, Any]:







			"""simple docstring"""
			a__  :		Union[str, Any]					=    create_accelerator(even_batches=lowerCAmelCase__)

			a__  :		Optional[int]					=    torch.nn.Linear(1		,  1)
			a__  :		Union[str, Any]					=    accelerator.prepare(lowerCAmelCase__)

			a__  :		Tuple					=    create_dataloader(lowerCAmelCase__		,  dataset_size=3		,  batch_size=1)

			a__  :		int					=    []
			with accelerator.join_uneven_inputs([ddp_model]):
						for batch_idx, batch in enumerate(lowerCAmelCase__):
									a__  :		Any					=    ddp_model(batch[0].float())
									a__  :		Any					=    output.sum()
									loss.backward()
									batch_idxs.append(lowerCAmelCase__)

			accelerator.wait_for_everyone()

			if accelerator.process_index == 0:
						assert batch_idxs == [0, 1]
			elif accelerator.process_index == 1:
						assert batch_idxs == [0]

def       lowerCAmelCase_	(    _lowercase							:  str)				->			Optional[int]:







			"""simple docstring"""


			with warnings.catch_warnings(record=lowerCAmelCase__) as w:
						with accelerator.join_uneven_inputs([Mock()]):
									pass

						assert issubclass(w[-1].category		,  lowerCAmelCase__)
						assert "only supported for multi-GPU" in str(w[-1].message)

def       lowerCAmelCase_	(    )				->			Dict:







			"""simple docstring"""
			a__  :		Optional[int]					=    True
			a__  :		Union[str, Any]					=    False
			a__  :		Optional[Any]					=    create_accelerator(even_batches=lowerCAmelCase__)
			a__  :		str					=    torch.nn.Linear(1		,  1)
			a__  :		int					=    accelerator.prepare(lowerCAmelCase__)
			a__  :		int					=    create_dataloader(lowerCAmelCase__		,  dataset_size=3		,  batch_size=1)
			a__  :		List[Any]					=    create_dataloader(lowerCAmelCase__		,  dataset_size=3		,  batch_size=1)

			with accelerator.join_uneven_inputs([ddp_model]		,  even_batches=lowerCAmelCase__):
						a__  :		str					=    train_dl.batch_sampler.even_batches
						a__  :		Dict					=    valid_dl.batch_sampler.even_batches

			assert train_dl_overridden_value == overridden_even_batches
			assert valid_dl_overridden_value == overridden_even_batches
			assert train_dl.batch_sampler.even_batches == default_even_batches
			assert valid_dl.batch_sampler.even_batches == default_even_batches

def       lowerCAmelCase_	(    )				->			str:







			"""simple docstring"""
			a__  :		Optional[int]					=    True
			a__  :		Tuple					=    False
			a__  :		int					=    create_accelerator(even_batches=lowerCAmelCase__)
			a__  :		str					=    torch.nn.Linear(1		,  1)
			a__  :		Optional[int]					=    accelerator.prepare(lowerCAmelCase__)
			create_dataloader(lowerCAmelCase__		,  dataset_size=3		,  batch_size=1		,  iterable=lowerCAmelCase__)
			a__  :		Dict					=    create_dataloader(lowerCAmelCase__		,  dataset_size=3		,  batch_size=1)

			with warnings.catch_warnings():
						warnings.filterwarnings("""ignore""")
						try:
									with accelerator.join_uneven_inputs([ddp_model]		,  even_batches=lowerCAmelCase__):
												a__  :		List[Any]					=    batch_dl.batch_sampler.even_batches
						except AttributeError:
									# ensure attribute error is not raised when processing iterable dl
									raise AssertionError

			assert batch_dl_overridden_value == overridden_even_batches
			assert batch_dl.batch_sampler.even_batches == default_even_batches

def       lowerCAmelCase_	(    )				->			Tuple:







			"""simple docstring"""
			a__  :		str					=    create_accelerator()
			a__  :		Dict					=    torch.nn.Linear(1		,  1)
			a__  :		str					=    accelerator.prepare(lowerCAmelCase__)
			create_dataloader(lowerCAmelCase__		,  dataset_size=3		,  batch_size=1		,  iterable=lowerCAmelCase__)

			with warnings.catch_warnings(record=lowerCAmelCase__) as w:
						with accelerator.join_uneven_inputs([ddp_model]		,  even_batches=lowerCAmelCase__):
									pass

						assert issubclass(w[-1].category		,  lowerCAmelCase__)
						assert "only supported for map-style datasets" in str(w[-1].message)

def       lowerCAmelCase_	(    )				->			Any:







			"""simple docstring"""
			a__  :		Any					=    create_accelerator()

			accelerator.print("""Test that even_batches variable ensures uniform batches across processes""")
			test_default_ensures_even_batch_sizes()

			accelerator.print("""Run tests with even_batches disabled""")
			test_can_disable_even_batches()

			accelerator.print("""Test joining uneven inputs""")
			test_can_join_uneven_inputs()

			accelerator.print("""Test overriding even_batches when joining uneven inputs""")
			test_join_can_override_even_batches()

			accelerator.print("""Test overriding even_batches for mixed dataloader types""")
			test_join_can_override_for_mixed_type_dataloaders()

			accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders""")
			test_join_raises_warning_for_iterable_when_overriding_even_batches()

			accelerator.print("""Test join with non DDP distributed raises warning""")
			a__  :		Optional[int]					=    accelerator.state.distributed_type
			a__  :		Optional[Any]					=    DistributedType.FSDP
			test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase__)
			a__  :		Union[str, Any]					=    original_state


if __name__ == "__main__":
		main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






